Greedy algorithms are optimization algorithms that make the locally optimal choice at each step with the hope of finding a global optimum, used in problems like coin change and minimum spanning trees.

Greedy algorithms are a class of algorithms that make a series of choices, each of which looks best at the moment, with the hope of finding an optimal solution to a problem. Unlike dynamic programming, which considers all possible solutions and stores intermediate results to find the best overall solution, greedy algorithms focus on making the locally optimal choice at each step without considering the larger problem.

The effectiveness of greedy algorithms depends heavily on the specific problem being solved. They are particularly useful for optimization problems where a locally optimal choice leads to a global optimum. Common examples include the coin change problem, where the goal is to minimize the number of coins used to make a given amount using the highest denominations first; Kruskal's and Prim's algorithms for finding minimum spanning trees; and Huffman coding for data compression.

While greedy algorithms are generally more straightforward and faster than dynamic programming approaches, they do not always yield the optimal solution. It's crucial to analyze the problem carefully to determine if a greedy approach is appropriate. In some cases, greedy algorithms may lead to suboptimal results, especially in problems where decisions made early on can adversely affect later outcomes.

Understanding greedy algorithms is essential for algorithm design and problem-solving in computer science, as they provide an efficient approach to a wide range of problems when applicable.